GALE: Geometric Active Learning for Search-Based Software Engineering
GALE: Geometric Active Learning for Search-Based Software Engineering
复制标题
GALE：基于搜索的软件工程的几何主动学习
DOI：
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发表时间：
2015
影响因子：
7.4
通讯作者：
M. Davies
中科院分区：
文献类型：
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作者：
Joseph Krall;T. Menzies;M. Davies
多目标进化算法（MOEAS）可帮助软件工程师找到解决复杂问题的新颖解决方案。当自动工具探索太多选项时，它们的使用缓慢且难以理解。大风是一个接近线性的时代Moea，它可以建立沿帕累托边境最佳解决方案表面的分段近似。对于每件作品，大风将解决方案朝着更好的目的而变化。在众多案例研究中，大风发现使用标准方法（NSGA-II，SPEA2）的可比解决方案使用较少的评估（例如20个评估，而不是1,000个评估）。当模型评估昂贵时，或者某些受众需要浏览并了解MOEA如何得出结论时，建议使用大风。
Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When automatic tools explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1,000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.